Optimisation Algorithms Professors and Supervisors in Sweden

Elina Rönnbergمشاهده پروفایل

استاد

Linköping UniversitySweden

Elina Rönnberg is a Professor and Deputy Head of Department at the Department of Mathematics, Linköping University, where she leads research in discrete optimisation and intelligent decision-making. Her work bridges theoretical method development and real-world applications in sectors such as healthcare, aviation, mining, and transportation. She is actively involved in the Wallenberg AI, Autonomous Systems and Software Program (WASP) and has collaborated with industry leaders like Saab and Scania. Her research focuses on advanced optimisation techniques including Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition. She also explores hybrid methods combining mathematical programming with constraint programming and machine learning. Applications span nurse rostering, electric vehicle routing, aircraft arrival scheduling, and underground mine planning. Recent publications highlight a strong trend toward integrating AI and machine learning—particularly graph neural networks—with classical optimisation frameworks to accelerate solution methods. Her work emphasizes practical impact, robustness, and scalability in solving complex scheduling and resource allocation problems. Nurse Rostering with Strategic Planning of Skills for Sick-Leave Robustness (2024) Pricing for the EVRPTW with Piecewise Linear Charging (2024) Speeding Up Logic-Based Benders Decomposition with Graph Neural Networks (2024) Elina supervises several PhD students and has co-supervised doctoral research at international institutions including Makarere University (Uganda) and the University of Exeter (UK). She has contributed to applied projects through student theses in collaboration with Scania and Saab, focusing on electric vehicle routing and search-and-rescue optimisation. She previously served as a Specialist in Optimisation at Saab Aeronautics (2014–2020) and co-founded Schemagi, a scheduling tool aimed at improving quality in healthcare. She teaches courses such as Introduction to Optimization (TAOP07) and Project - Applied Mathematics (TATA62). Her research group, 'Mathematics and algorithms for intelligent decision-making,' operates within the Division of Applied Mathematics (TIMA) at the Department of Mathematics. The team develops decision support tools that enhance efficiency and sustainability in complex systems, particularly under the growing demands of electrification and digitalisation in transport and logistics.

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Pierre Flenerمشاهده پروفایل

استاد

Uppsala UniversitySweden

Pierre Flener is a Professor at the Department of Information Technology, Division of Computing Science at Uppsala University. He leads the Optimisation Group and is a member of the Centre for Interdisciplinary Mathematics. His work focuses on constraint programming and discrete optimization, addressing complex scheduling, routing, and resource allocation challenges. Flener is an Officer of the Order of Merit of Luxembourg and co-founder of NordConsNet, the Nordic Network for Constraint Programming researchers. Research Interests: Flener’s research spans constraint programming, combinatorial optimization, and algorithm design. He develops models and tools for automated decision-making in domains like air traffic management, sensor networks, and industrial robotics. His work emphasizes practical applications, leveraging constraint satisfaction techniques to solve real-world puzzles such as vehicle routing and personnel allocation. Key Contributions: Flener has authored over 100 publications on constraint solving, symmetry breaking, and CP-based approaches to industrial problems. Notable projects include airspace sectorization optimization, energy-efficient sensor networks, and financial portfolio design. He has led initiatives like Auto-Tabling for MiniZinc and collaborated on CP applications in bioinformatics and image processing. Labs & Teams: He heads the Optimisation Group at Uppsala, fostering research in CP and its applications. NordConsNet, co-founded by Flener, connects Nordic researchers and practitioners in constraint technology.

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Sigrid Källblad Nordinمشاهده پروفایل

دانشیار

KTH Royal Institute of TechnologySweden

Sigrid Källblad Nordin is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Department of Mathematics (Division of Probability, Mathematical Physics, and Statistics). Her research focuses on Mathematical Finance, Probability Theory, and Stochastic Analysis, with an emphasis on measure-valued processes, martingale optimal transport, and model uncertainty. She holds a DPhil from the University of Oxford (2014). Her work bridges theoretical advancements in stochastic control, optimization, and financial applications. Recent research includes Bayesian optimal adaptive control, robust option pricing, and dynamically consistent investment strategies under uncertainty. She teaches courses such as Financial Mathematics and Financial Derivatives, and supervises PhD students Linn Engström and Chaorui Wang. Publications span journals like Annals of Applied Probability , Finance and Stochastics , and SIAM Journal on Control and Optimization , reflecting contributions to optimal transport, stochastic processes, and financial modeling. She is currently hiring a new PhD student and welcomes inquiries about master thesis supervision.

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Trisha Rajputمشاهده پروفایل

استادیار

University of GothenburgSweden

Trisha Rajput is an Assistant Professor at the School of Business, Economics and Law , University of Gothenburg , where she teaches international trade law to both law and management students. Her research focuses on international economic law, with particular emphasis on trade facilitation, global value chains and the intersection of law and digital technology in international commerce. Education Doctor of Laws (PhD), University of Leeds, UK – thesis on WTO coordination with non-WTO regimes in public international law. LL.M. in European and International Business Law, University of Leeds, UK. LL.B., India – prior lecturing experience at Rajiv Gandhi National University of Law. Research Interests Dr Rajput’s work lies at the nexus of international trade law , public international law and emerging digital technologies . Current projects investigate how digital infrastructures—ranging from AI-based transport systems to blockchain-enabled single-window environments—can enhance supply-chain finance for SMEs along the China-Europe railway corridors. Her scholarship also critically examines normative conflicts between WTO rules and other specialist international regimes. Publications Overview Spanning 2013-2024, her publications reveal a consistent focus on (i) trade-facilitation mechanisms (single-window reforms, paperless trade initiatives), (ii) regulation of risk in multimodal transport, and (iii) technology governance such as AI transparency in customs and blockchain adoption in export-oriented firms. Collaborative work with scholars from law, logistics and management disciplines underlines an interdisciplinary approach. Research Funding & Projects European Union funded project on trade facilitation and global value chains. Principal investigator, “Role of Digital Infrastructures in Financing SMEs” (2019-2022). Co-investigator, “Transition to AI-based Transport Systems” (2020-2021). Contact & Location Room C620, Department of Law, Vasagatan 1, SE-411 24 Göteborg, Sweden. Email: trisha.rajput@law.gu.se

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Chuan Heمشاهده پروفایل

استادیار

Linköping UniversitySweden

Chuan He is an Assistant Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA) and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research bridges continuous optimization and machine learning, focusing on algorithmic efficiency and theoretical foundations. Education: Ph.D. in Industrial and Systems Engineering, University of Minnesota, USA (2019–2023) B.S. in School of Mathematical Sciences, Xiamen University, China (2015–2019) His research interests include deep learning, decentralized and large-scale optimization, high-order methods, and applications in healthcare, scientific computing, and engineering. He develops algorithms with strong theoretical guarantees, particularly in nonconvex optimization settings. His work emphasizes improving the speed, reliability, and scalability of machine learning training processes. His recent publications focus on Newton-CG based methods, augmented Lagrangian techniques, and federated learning under constraints. These contributions span top journals in operations research, optimization, and machine learning, reflecting a strong trend toward integrating second-order optimization with practical machine learning challenges. Scientific Awards: No awards explicitly mentioned in the text. Chuan He advises and collaborates within the WASP Mathematics research environment, particularly in the 'Optimisation for machine learning' group. He previously held a postdoctoral position at the University of Minnesota under Professor Ju Sun. His research is supported through institutional affiliations with WASP and Linköping University. He actively contributes to the academic community through conference presentations at INFORMS, SIAM, and NeurIPS workshops. He is involved in the 'Optimisation for machine learning' research group at MAI, which aims to develop more efficient and theoretically sound algorithms for machine learning. The group focuses on replacing heuristic methods with principled approaches, reducing computational costs in training models.

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Justin Pearsonمشاهده پروفایل

دانشیار

Uppsala UniversitySweden

Justin Pearson is an Associate Professor at the Department of Information Technology; Division of Computing Science at Uppsala University . He is a member of the university's optimisation group and coordinates the IT department's mentor programme for new employees . Research Interests include Constraint Programming , Combinatorial Optimisation , Artificial Intelligence , Software Testing , and Complexity Theory . His work focuses on theoretical and practical aspects of constraint satisfaction, local search algorithms, and symmetry breaking in constraint programming. Teaching involves courses such as Algorithms and Data Structures II (1DL231) and Introduction to Machine Learning for Bachelor Students (1DL034), with a PhD-level course on Category Theory offered periodically based on demand. Scientific Contributions span publications in constraint programming for air traffic management, sensor networks, and industrial applications. Recent work includes parameterised treewidth in constraint models, time-series constraints, and symmetry breaking techniques. Students supervised include PhD candidates Frej Knutar Lewander (co-supervised with Pierre Flener), Yi Zhao (co-supervised with Di Yuan), and previously graduated students Gustav Björdal , María Andreína Francisco Rodríguez , and Joseph Scott .

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Calvin Tsayمشاهده پروفایل

مدرس

KTH Royal Institute of TechnologySweden

Calvin Tsay serves as a Lecturer in the Department of Computing at Imperial College London, supported by the BASF/RAEng Senior Research Fellowship in Scale-Bridging Modelling and Optimisation. Previously, he held the EPSRC David Clarke Fellowship and Imperial College Research Fellowship. His academic foundation includes BS and BA degrees from Rice University (USA) and a PhD in Chemical Engineering from The University of Texas at Austin. His educational background comprises: BS and BA from Rice University, USA PhD in Chemical Engineering from The University of Texas at Austin Tsai's research bridges machine learning and mathematical optimisation, focusing on optimisation algorithms, process dynamics/control, and process systems engineering. He specifically targets mixed-integer programming applications where machine learning's scalability meets optimisation's rigour, developing novel algorithmic frameworks for complex engineering systems. His scientific recognition includes: BASF/RAEng Senior Research Fellowship in Scale-Bridging Modelling and Optimisation EPSRC David Clarke Fellow Imperial College Research Fellow Current funding stems from the BASF/RAEng Senior Research Fellowship, building on prior EPSRC and Imperial College Research Fellowships. As an SEC scholar (August-September 2024), he collaborates with KTH's Digital Futures initiative to establish sustainable research pipelines, though student advising details remain unspecified in source materials. He operates within Imperial's optimisation research group and actively partners with KTH's Digital Futures team during his SEC scholar visit, co-developing cross-institutional methodologies with Dr. Jan Kronqvist to synergise machine learning and mathematical optimisation approaches.

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